Connections to Sampling Based Trajectory Optimization: Local optimization techniques like CCP, SQP rely heavily on the quality of initialization. One way to mitigate this is to use derivative-free sampling-based optimal control [1]}. CEM [2]}, [3]} is one such method and has recently been combined with local minimizers like gradient-descent [4]}. Authors in [1]} showed that their niche trajectory sampling coupled with gradient-descent could lead to solutions in different homotopies. Our algorithm leads to a similar result but with a very different process. We sample trajectories from the Gaussian distribution proposed in [6]} but only once and then initialize a powerful multi-convex local minimizer from those samples.
